Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence
AbstractFinancial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has emerged in the last decade. In this paper we review the estimation of a variety of highly flexible stochastic volatility models, and introduce some efficient algorithms based on recent advances in state space simulation techniques. These estimation methods are illustrated via empirical examples involving precious metal and foreign exchange returns. The corresponding Matlab code is also provided.
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Bibliographic InfoPaper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2013-74.
Length: 23 pages
Date of creation: Nov 2013
Date of revision:
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stochastic volatility; scale mixture of normal; state space model; Markov chain Monte Carlo; financial data;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-12-06 (All new papers)
- NEP-ECM-2013-12-06 (Econometrics)
- NEP-ETS-2013-12-06 (Econometric Time Series)
- NEP-ORE-2013-12-06 (Operations Research)
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